US10635842B2 - Method of identifying technical design solutions - Google Patents
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- US10635842B2 US10635842B2 US15/638,985 US201715638985A US10635842B2 US 10635842 B2 US10635842 B2 US 10635842B2 US 201715638985 A US201715638985 A US 201715638985A US 10635842 B2 US10635842 B2 US 10635842B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2219/00—Indexing scheme relating to application aspects of data processing equipment or methods
- G06F2219/10—Environmental application, e.g. waste reduction, pollution control, compliance with environmental legislation
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- the present invention relates to a method of identifying technical design solutions in order to reach pre-determined design performance targets, such as greenhouse gas (GHG) emission targets.
- the invention also relates to a design apparatus and to a computer program product for carrying out the method.
- the first obstacle is the lack of publications related to low carbon building references, and which could be used in the design process.
- Architectural design is an iterative process between problems and solutions. The more iterations, the better the architectural design brief can be designed, for finding a proper solution. Therefore, many types of publications are commonly used as metaphors to transform the design brief into first solutions. Designers commonly prefer aesthetic aspects to ethical aspects.
- the lack of publications can be explained by the following two reasons:
- LCA life cycle assessment
- ecobalance a technique for assessing environmental impacts associated with all the stages of a building's life from cradle to grave (i.e. from raw material extraction through materials processing, manufacture, distribution, use, repair and maintenance, to disposal or recycling).
- LCA is time consuming, because of the necessity to describe dozens or hundreds of building elements. As a consequence, it reduces the possibility of implementing an iterative process, which is crucial for project quality.
- the third obstacle is the uncertainty about the design at an early stage. At this moment, largely incompatible needs co-exist. While robust and reliable LCA requires a high resolution of details of a building project, early design stage implies a low detail resolution. However, it is necessary to perform an LCA early in the design process. If an LCA is performed late in the design process, it decreases the usability of its results for impacting the design. Thus, performing an LCA at an early design stage remains a real challenge. So far, the main ways of tackling this issue have been the LCA methodology improvement, and simplification by reducing the scope of the analysis (over-simplification) by transforming building components into macro-components (sets of components), and implementing data acquisition with computer aided design tools. However, the end result is not precise enough. Simplified techniques can provide results which deviate by as much as 30% from those of a detailed LCA. Moreover, simplification decreases the usability of the LCA because it would then be more difficult to interpret the results.
- the fourth obstacle is the non-reproducibility of LCA results. This is due to the method itself, which allows designers to define their own system boundaries (i.e. how extensive the LCA is) and to choose an LCA database (comprising environmental impacts of individual building elements or systems). Thus, two different designers performing an LCA on the same building will produce two different results if the boundaries and LCA database are not clearly specified in the design brief.
- the proposed solution has the advantage that it allows a designer to work with design parameters, which have the greatest impact on the performance of the building. Furthermore, the solution allows an LCA to be carried out at an early stage in the design process. Moreover, there is no need to perform a highly time-consuming iterative design process, which typically leads to an optimized proposition regarding a single design target, but with a high probability of missing other building constraints a designer has to face. By contrast, the present method allows the designer to modify a large number of parameters so that the consequences of these modifications may immediately be made explicit to the designer. The proposed solution enables a high level of understanding of the climate change complexity at an early design stage.
- a computer program product comprising instructions for executing the method according to the first aspect of the present invention.
- FIG. 1 is a flow chart illustrating a design method according to one example of the present invention
- FIG. 2 illustrates a simplified block diagram of the design system where the teachings of the present invention may be applied
- FIG. 3 illustrates a parallel coordinate plot illustrating some design solutions visually
- FIG. 4 schematically illustrates a target cascading approach used to divide the building level emission targets into sub-targets.
- G1 The method should be able to provide design alternatives or references (sets of design parameters) which relate to climate change issues.
- G2 The number of references should be as large as possible. The higher the number of references, the easier it is for designers to find a reference that matches every constraint.
- G4 The reference database should be generated before starting the design stage in order to facilitate the number of iterations during the design process with instantaneous feedback.
- the purpose of the proposed architectural pre-design method is to provide a database of references or design alternatives, specifically generated per project according to their characteristics in terms of usage and location.
- Each reference is in this context a combination of design parameters with their corresponding predicted GHG emissions, specified for a unique project.
- the database can be explored by designers before or during the design process. Instantaneous overview and exploration of this reference database by data visualisation enables a better understanding, at an early stage, of the architectural consequences of increasingly GHG emission targets.
- the proposed method in its detailed form is a combination of sensitivity analysis, as explained later, with LCA and data visualization techniques.
- the process starts at step 11 , where a designer or a data processing unit 5 selects a schematic building model, which in this example is a three-dimensional (3D) model, a purpose of the building (e.g. residential or office building) and the geographical location of the building.
- a schematic building model is generated.
- a feasibility study of the building may already have been carried out before step 11 .
- the aim of the feasibility study is to determine whether or not it is possible to build the planned building on a particular piece of land.
- the feasibility study may be a very rough process, where a model used involves only surfaces and volumes.
- a first database 1 referred to as a database of design parameters
- the designer or the data processing unit 5 selects a set of design parameters which influence the building's GHG emissions.
- the data processing unit 5 may automatically select some parameters which, for example, have in the past been selected for similar buildings, or the same designer has selected in the past. It is possible that the system automatically proposes some design parameters for the designer to simplify the designer's task.
- the data processing unit 5 qualifies and quantifies the selected design parameters into a range of qualities and quantities. For example, windows may be qualified to single, double or triple glazing, while the window to wall ratio (VWVR) south may be quantified into 50%, 75% and 100%.
- the data processing unit 5 generates some combinations of the selected design parameters. This can be done by a sensitivity analysis, or more specifically by running a part of the sensitivity analysis which is a technique used to determine how different values of an independent input will impact a particular dependent output under a given set of assumptions.
- the sensitivity analysis used at this stage may include the following steps:
- a sensitivity analysis may be used to rank (step 6 above) design parameters according to their environmental impacts.
- a sensitivity analysis determines the contribution of the individual design variable or parameter to the total performance (in this example GHG emissions) of the design solution. It can be used to determine which design variables or subset of design variables account for the majority of the building performance variance (and optionally in what percentage).
- Sensitivity analyses are often grouped into three classes: screening methods, local sensitivity methods and global sensitivity methods. Next, a sensitivity analysis based on a screening method is explained in more detail. Screening methods are used for complex situations which are computationally expensive to evaluate and/or have a large number of design parameters. It is an economical method, which can identify and rank qualitatively the design parameters which control most of the output variability, i.e. the performance.
- a performance estimation using “standard values” is used. For each design parameter, usually two extreme values are selected on both sides of the standard value. The differences between the result obtained by using the standard value and those obtained using the extreme values are used to determine to which design parameters the building energy performance is significantly sensitive.
- the question(s) to be answered by the analysis is/are identified, i.e. the output variable or parameter is defined.
- the analysis focuses on the building energy performance (e.g. kWh/(m2 year)).
- the building costs may be linked to the sensitivity analysis and may form an integrated part of the entire decision process.
- An appropriate simulation model including its design variables is also selected in this step. Based on the output of a simulation model, it is possible to answer the identified question.
- the required level of modelling detail depends on the design phase where the sensitivity analysis is applied, as well as on the available knowledge of design parameters. In the very early conceptual or preliminary design phases, relatively simple calculation methods are used because the design solutions are not well defined and the knowledge of design parameters is limited, while in later design phases more detailed models may be used.
- a second step it is determined which design parameters should be included in the sensitivity analysis. This is done by a one-parameter-at-a-time method in which the effect of each design parameter on the building performance is evaluated in turn.
- a performance estimation using “standard values” for all design parameters is used.
- For each design parameter usually two extreme values are selected, i.e. one on both sides of the standard value. The differences between the results obtained using the standard value and those obtained using the extreme values are compared to evaluate to which design parameter the building energy performance is significantly sensitive.
- a design parameter can be considered to be sensitive, if its value varies substantially. These design parameters are the ones selected for the initial screening.
- a simple method of determining the design parameter sensitivity is to calculate the output % difference for the extreme values of the design parameter. This “sensitivity index” can be calculated as
- E max and E min represent the maximum and minimum output values, respectively, resulting from varying the design parameter over its entire range. If the sensitivity index reaches a defined threshold value, the design parameter is considered to be important and is included in the further analysis.
- a probability density function is assigned to each design parameter, which is found to be important for building the energy performance in the initial screening.
- the typical value chosen, the variation limits and the probability distribution may depend on architectural considerations, technical possibilities or limitations and/or economical consideration or other issues. Results of sensitivity analysis generally depend more on the selected ranges than on the assigned probability distributions. Typically, three different probability density functions are used; uniform, lognormal and normal distribution.
- a fourth step input vectors are generated.
- Various sampling procedures exist such as: random sampling, latin hypercube sampling and quasi-random sampling. Control of correlation between variables within a sample is important and difficult, because the imposed correlations have to be consistent with the proposed variable distribution.
- the factorial sampling method proposed by Morris M D. “Factorial sampling plans for preliminary computational experiments”, Technometrics 1991; 33(2): 161-74 may be applied to generate the input vectors.
- the method comprises a number of individually randomized one-factor-at-a-time samples of design parameters where all parameters are varied within their variable space in a way that spans the entire space to form an approximate global sensitivity analysis.
- random samples of design parameters are generated. Initially, each design variable is scaled to have a region of interest equal to [0,1] according to the probability density function chosen for each variable.
- Each design parameter may assume a discrete number of values, called levels, l, with a distance of equal size, D.
- a design parameter vector, X i with a number of elements equal to the number of design parameters, k, is assigned a random base value (on a discretized grid). Then a path of orthogonal steps through the k-dimensional parameter space is “followed”. The order of the steps is randomized by selecting a new randomized value for one randomized parameter at a time, while keeping all other design parameters constant. After each step, a new design parameter vector is defined. This is continued until all design parameters are represented by two different values creating a set of (k+1) independent design parameter vectors. The procedure is repeated r times, creating a set of r(k+1) independent design parameters vectors.
- an output variable is created for each sample of the design parameters represented in a design parameter vector. This may be achieved by the selected simulation model.
- a sixth and last step the influence of each design parameter on the expected value and the variance of the output parameter(s) are assessed.
- a number of different techniques can be used, such as rank transformation, regression analysis and scatter plots, all giving different measures of sensitivity.
- the main purpose of the method is to determine which design parameters may be considered to have effects which are a) negligible, b) linear and additive, or c) nonlinear or involved in interactions with other factors.
- the elementary effect for the ith input parameter in a point x is
- E ⁇ ⁇ E ⁇ ( x 1 , ... ⁇ , x k ) y ⁇ ( x 1 , x 2 , ... ⁇ , x i - 1 , x i + D , x i + 1 , ... ⁇ , x k ) - y ⁇ ( x 1 , ... ⁇ , x k ) D . ( 2 )
- a number of the elementary effects EE i of each design parameter is calculated based on the generated samples of each design parameter in step four, i.e. the chosen value of r.
- the model sensitivity to each design parameter is evaluated by the mean value and the standard deviation of the elementary effects:
- ⁇ the mean value of the absolute values of the elementary effects determining if the design parameter is important
- ⁇ the standard deviation of the elementary effects, which is a measure of the sum of all interactions of x i with other factors and of all its non-linear effects.
- r is the number of elementary effects investigated for each parameter or the number of repetitions of the procedure in step four.
- the result of the sensitivity analysis is a list of important design parameters and a ranking of the design parameters by the strength of their impact on the output, ⁇ .
- the data processing unit 5 attributes the combinations of the design parameters (which have been qualified and/or quantified) to the building's 3D model.
- This provides design alternatives described by the design parameters and/or design components and systems. Each design alternative is in fact a building-specific 3D model with a combination of the different design parameters.
- a design component is something on which GHG emissions can be assessed.
- a design parameter influences the performance (in this example the GHG emissions).
- a wall is a design parameter and component, because GHG emissions can be assessed for a wall, and it influences the performance of the building.
- a natural ventilation or lighting control are only design parameters, but they are not design components because GHG emissions cannot be assessed for them.
- Tables 1 and 2 below give some examples of design components and design parameters and how they can be quantified into a range of numerical values.
- step 21 the LCA is carried out by the data processing unit 5 for the design alternatives to determine GHG emissions of the different design alternatives.
- a second database 2 referred to as a life cycle inventory database
- the life cycle inventory database 2 includes various design parameters and components and their environmental impacts.
- One life cycle inventory database 2 may typically be used for various kinds of buildings.
- the LCA is carried out as a part of the sensitivity analysis (step 6 described earlier). The building's lifetime is considered to be 60 years.
- the life-cycle impacts are calculated from the decomposition of the design alternatives into building components and systems. The following equation may be used for this calculation:
- I f [kg CO2—eq] is the environmental impact of building f
- n [unity] is the number of components and systems into which the building is decomposed
- p [unity] is the number of different types of energy demand
- m i is the mass or quantity of components or systems i;
- LB LM i is the lifetime of the building
- LM i [years] is the lifetime of the component or system i
- C t [MJ] is the consumption of the energy in the operating phase of the building
- K f , t ⁇ [ kg ⁇ ⁇ CO ⁇ ⁇ 2 - eq MJ ] is the environmental impact f for the unit energy t.
- a third database 3 referred to as a design alternative database, is generated.
- This database comprises the life cycle environmental impacts of the different combinations of the design parameters.
- the data processing unit 5 ranks the design parameters of all these design alternatives according to their contribution to the GHG emissions. This step may comprise generating an ordered list of design parameters, where the design parameters are ordered for example in a descending order according to their GHG emission impacts.
- the data processing unit 5 or the designer selects a set of design parameters according to their GHG emission impacts by ignoring design parameters which have only very little impact on the overall performance (GHG emissions).
- step 29 the set of design parameters are visualized (for example substantially in real time) by the data processing unit 5 by using a data visualisation technique, such as a parallel coordinate plot.
- a data visualisation technique such as a parallel coordinate plot.
- FIG. 3 A part of an example parallel coordinate plot is shown in FIG. 3 .
- Various design parameters are represented by the vertical axes.
- the right-hand-most vertical line represents the life-cycle GHG emissions (global warming potential (GWP)).
- GWP global warming potential
- Parallel coordinates make it possible to easily explore and understand multi-dimensional numerical datasets.
- Each data point in the dataset may be represented as a polyline plotted according to n parallel lines corresponding to the n dimensions of the data.
- the parallel lines may be presented vertically and equally spaced.
- the polylines are drawn along the horizontal axe with vertices crossing the vertical parallel lines at the position that correspond to the relative value of this data point for the considered dimension.
- One of the advantages of this technique is that the axes can be arranged in different ways, in order to group, for instance, similar dimensions to present data first according to the most discriminant dimensions or to identify correlations between pairs of dimensions. It can be used in combination with other visualisation techniques using link and brush mechanisms, and in conjunction with mining techniques for instance to highlight clusters of data.
- step 31 the designer explores the design alternatives.
- the designer may apply constraints on the GHG emissions and/or on one or more of the design parameters and then see what design alternatives remain possible. Once the constraints have been applied, the results can be viewed substantially in real time.
- the solid polylines illustrate design alternatives, which fulfil a design constraint of GWP below 80 kg CO2/person year.
- the dotted polylines show the design alternatives for which GWP ⁇ 80 kg CO2/person year.
- step 33 the data processing unit 5 assesses the relative weight (in percentage) of the GHG emissions of the design components and systems in the design alternative database 3 . This is done based on the LCA carried out on the design alternatives in step 21 .
- step 35 the data processing unit 5 determines performance targets (in this example GHG emission targets) for the design components and systems in the design alternative database.
- step 37 the designer uses the GHG emission targets for the design components and systems in the design process. More concretely, the designer may now introduce one or more new design component(s) and/or system(s) and they would know the emission target the new design component(s) and/or system(s) should fulfil.
- the steps 33 , 35 , and 37 may be carried out by a process called target cascading.
- target cascading Once the LCA performances of the design alternatives have been obtained, it is possible to apply the target cascading, and more specifically an approach called “top down and bottom up” to this population (design alternatives), which is specifically developed for each new project.
- top down refers to the fact that an objective is fixed at the building level for GHG emissions.
- Bottom up refers to the fact that the objective is divided into sub-targets at the component or system level according to the relative weight of the average GHG emission impacts of these components and systems.
- the target cascading approach is schematically illustrated in FIG.
- the target cascading approach is an interesting way of simplifying the building's LCA by decomposing the building into components and systems (or sub-systems).
- Target cascading guides designers towards optimal targets at the component and/or system level, allowing them to assess smaller perimeters than the entire building, and attributes responsibilities to all the design team members.
- the target cascading approach is done dynamically, i.e. it is possible to change the targets of the components and/or systems. Changing one target also changes at least one of the remaining targets to compensate the target which was first changed.
- the target cascading is static, i.e.
- the designers do not have the ability to play with the sub-targets (at the component and system level), even if they reach the global target (at the building level). For this reason, in a static approach, it would not be possible to implement a bad technology, even if it were possible to compensate for it with other very efficient components and/or systems.
- a dynamic target cascading approach the designer's freedom to choose new components is not restricted as long as the global target at the building level can be achieved.
- the above described method may be modified by for example so that after step 25 , the process continues in step 13 but so that the design parameters are selected based on the ranking obtained in step 25 . In other words, the design parameters that have only little impact on the GHG emissions may be ignored. The process thus only concentrates on the design parameters which have the greatest importance as far as the GHG emissions are concerned. Furthermore, as the process continues, it is possible to apply a second sensitivity analysis method for example in steps 19 , 21 and 25 as these steps are carried out for the second time. In this manner, it would be possible to obtain more combinations than with the Morris approach explained above, and the design alternative database would be updated accordingly.
- the second sensitivity analysis may be a variance-based sensitivity analysis, which is a form of global sensitivity analysis.
- Sobol method such as disclosed for example in “A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis”, Rafael Rosolem et al., Journal of geophysical research , vol. 117, D07103, 2012.
- the Sobol method uses the Monte Carlo strategy and gives quantitative and highly reliable results. Also, it is possible to understand interaction between the input parameters. However, comparing to the Morris method, the computational time is increased by a factor 100.
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Abstract
Description
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- The recent awareness of climate change limits the number of publications.
- The constant progression of climate change objectives quickly renders the few available publications quickly obsolete.
-
- the lack of publications about the climate change,
- the time required for environmental assessment,
- the uncertainties about the design at an early stage, and
- the non-reproducibility of the results.
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- 1. Identification of outputs;
- 2. Identification of inputs;
- 3. Definition of probability distribution and range of values for the inputs;
- 4. Choice of an approach;
- 5. Selection of parameter combinations and calculation of an output distribution given by a generated input matrix;
- 6. Assessment of the relative influence inputs/outputs.
where Emax and Emin represent the maximum and minimum output values, respectively, resulting from varying the design parameter over its entire range. If the sensitivity index reaches a defined threshold value, the design parameter is considered to be important and is included in the further analysis.
where μ is the mean value of the absolute values of the elementary effects determining if the design parameter is important, and σ is the standard deviation of the elementary effects, which is a measure of the sum of all interactions of xi with other factors and of all its non-linear effects. r is the number of elementary effects investigated for each parameter or the number of repetitions of the procedure in step four. The result of the sensitivity analysis is a list of important design parameters and a ranking of the design parameters by the strength of their impact on the output, μ.
TABLE 1 | |
Components | Main materials employed |
Backfill | Demolition of brick structure | |
Excavation | Mechanical | |
Foundations | Reinforced concrete, Bitumen waterproofing, | |
mortar | ||
Floors | Structure | Reinforced concrete or wood |
Insulation | Cellulose fibre, glass wool or polystyrene | |
Roof | Coverings | Concrete, mortar, plaster, parquet or ceramics. |
Walls | Structure | Reinforced concrete, brick, or wood |
Insulation | Cellulose fibre, glass wool, or polystyrene | |
Coverings | Polyethylene, plaster or mortar. | |
Windows | Single, double, triple glazing with wood, | |
aluminium or PVC frames | ||
Doors | Wood glazed door or not | |
TABLE 2 | |
Parameters | Values |
Shape | 1 | 2 | 3 | — | |
WWR* south | 50% | 75% | 100% | — | |
WWR east and west | 25% | 50% | 75% | 100% | |
WWR north | 20% | 40% | 60% | 80% | |
Windows type | double | triple | — | — | |
glazing | glazing | ||||
Frame quality | metal | PVC-XL* | wood + | ||
PUR* | |||||
|
5% | 10% | 15% | 20% | |
Rooftop PV* | 25% | 50% | 75% | 100% | |
Natural | 0% | 30% | 60% | 100% | |
ventilation ratio | SEA* | SEA | SEA | SEA | |
| SEA | 80% | 65% | 50% | |
schedule | SEA | SEA | SEA | ||
Lighted |
25% | 50% | 75% | 100% | |
surface | surface | surface | | ||
Appliances | SEA | ||||
80% | 60% | 40% | |||
380/4 | SEA | SEA | SEA | ||
380/4 | 380/4 | 380/4 | |||
Heating system | 0.005 | 0.01 | 0.02 | 0.05 | |
(kg CO2/MJ) | |||||
*Window to wall ratio (WWR), society of engineers and architects (SEA), photovoltaic (PV), polyvinyl chloride PVC-XL, polyurethane (PUR) |
where If [kg CO2—eq] is the environmental impact of building f; n [unity] is the number of components and systems into which the building is decomposed; p [unity] is the number of different types of energy demand; mi is the mass or quantity of components or systems i;
is the environmental impact f associated with the life cycle of one unit mass or quantity i;
is the largest integer not greater than
LB [years] is the lifetime of the building; LMi [years] is the lifetime of the component or system i; Ct [MJ] is the consumption of the energy in the operating phase of the building; and
is the environmental impact f for the unit energy t.
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EP16178041 | 2016-07-05 |
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CN111898190B (en) * | 2020-08-03 | 2024-02-06 | 西安建筑科技大学 | Method and equipment for determining outdoor calculation parameters of natural ventilation design |
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US8768655B2 (en) | 2010-09-29 | 2014-07-01 | Sefaira, Inc. | System and method for analyzing and designing an architectural structure using bundles of design strategies applied according to a priority |
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US8768655B2 (en) | 2010-09-29 | 2014-07-01 | Sefaira, Inc. | System and method for analyzing and designing an architectural structure using bundles of design strategies applied according to a priority |
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